It is sometimes convenient to digitize documents with a digital handheld camera. However, document capture with digital cameras has many inherent limitations. For example, it is difficult to project uniform lighting onto a document surface, and this often results in uneven illumination and color shift in the acquired pages. Another issue common with documents digitized with handheld digital cameras is that the text is often unclear and blurry due to movement of the camera in the user's hands.
These types of conditions make camera captured documents difficult to analyze and transform into useful electronic formats. For example, blurry or unclear text makes optical character recognition difficult, if not impossible, for purposes of transforming the digitized image into a text editable document.
One approach to addressing this issue is segmentation, commonly referred to as binarization in the document image analysis community, of the foreground and background of the document. Segmentation of the foreground and background is usually the first step towards document image analysis and recognition. For well-scanned documents with text on uniform background, high quality segmentation can be achieved by global thresholding. However, for camera-captured document images, non-uniform lighting is commonplace and global thresholding methods often produce unacceptable results. Adaptive thresholding algorithms have been developed; however, such programs can have difficulty handling documents containing figures.